Learning outcomes

This is how I intend to study the midterms by checking against learning objective. I should be able to understand the concept and fulfill the learning objective.

The first level checkbox is the learning objective, which is checked if I understand. The second bullet point is supplementary information that I have added. Content in bold are content that I may be unsure of.

Learning outcomesIntroductionMachine Learning Learning FrameworksPipelineDecision Theory Optimization RegressionModel Selection Regularization ClassificationLogistic RegressionSoftmax RegressionNeural NetworksFeedforward Networks Backpropagation Feature Engineering Convolutional Networks Recurrent Neural Networks Kernel MethodsMaximum Margins DualitySupport Vector MachinesKernelsKernelizationGraphical ModelsProbabilityGraph Theory Bayesian Networks (Directed Graphical Models)Markov Random Fields (Undirected Graphical Models)

Introduction

Machine Learning

Learning Frameworks

Pipeline

 

Decision Theory

 

Optimization

 

Regression

 

Model Selection

 

Regularization

 

Classification

 

Logistic Regression

 

Softmax Regression

 

Neural Networks

Feedforward Networks

 

Backpropagation

 

Feature Engineering

 

Convolutional Networks

 

Recurrent Neural Networks

 

 

Kernel Methods

Maximum Margins

 

Duality

Comment: As the two strategies are explained side-by-side, it was quite hard to follow. Moreover you are mixing primal-dual (from optimsation) and Lagrangian (from systems world).

Support Vector Machines

Kernels

Kernelization

 

Graphical Models

Probability

 

Graph Theory

 

Bayesian Networks (Directed Graphical Models)

 

Markov Random Fields (Undirected Graphical Models)

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